MalDataGen is an advanced Python framework for generating and evaluating synthetic tabular datasets using modern generative models, including diffusion and adversarial architectures.
NIST Collaborative Research Cycle on Synthetic Data. Learn about Synthetic Data week by week!
Predicting length of stay in hospitals intensive care unit using MIMIC IV
Wearable air-quality risk forecasting with rigor-first evaluation — persistence floors, rolling-origin CV, GAN/SMOTE ablation, conformal prediction. Ships pulsebench, a standalone toolkit for evaluating imbalanced time-series models.
A repository for the research article titled "DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection".
The Anonymous Synthesizer for Health Data
This is the repository to generate synthetic tabular data when the tabular data has imbalance in some feature.
The following repository contains the online appendix for the paper "Generative AI for Banks: Benchmarks and Algorithms for Synthetic Financial Transaction Data". It not only provides the python code for all experiments conducted but also background information for the literature review.
This repository contains the code of our published work in IEEE JBHI. Our main objective was to demonstrate the feasibility of the use of synthetic data to effectively train Machine Learning algorithms, prooving that it benefits classification performance most of the times.
SyntheticOcean: Open-Source Library for Generating Synthetic Tabular Data + SynDataGen (Framework for Synthetic Data Generation)
AI-powered credit card fraud detection system combining ML/DL models with explainable AI. Features hybrid ensemble (XGBoost + LSTM + AutoEncoders), synthetic data generation via CTGANs, and LLM-powered explanations. Achieves 99.2% accuracy with real-time Streamlit dashboard.
Worked on detecting illicit transactions in the Ethereum Transactions dataset by increasing our dataset size, and with little tolerance to missing fraudulent transactions.
Generate high-quality synthetic tabular datasets from CSV files using CTGAN, with automated evaluation metrics, visual comparisons, and downloadable results.
Differentially private synthetic tabular data generation using DP-CTGAN and diffusion models
M.T.D. is a project that tackles the growing risks faced by industries worldwide due to cyber threats. Our project employs a comprehensive approach to address various cyber-attacks, focusing on intrusion and malware threats by utilizing Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (A.I.).
[Dacon/KIST] 상추의 생육 환경 생성 AI 경진대회 2nd
SynthAI-PD: Explainable Gait-Based Model for Prodromal Parkinsonism Detection Using Wearable Sensors
GAN-based Deep Learning data synthesizer - Modeling Tabular data using Conditional GAN
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